Last updated: 2025-11-20
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Genotype data integration of ONT enrichment experiment with 10X expression data for AML4 sample
The genotype data has been analyzed using NanoGen, a custom pipeline designed to call mutations from targeted enrichment data. NanoGen follows a multi-step process to ensure accurate mutation detection. It starts by using Blaze to identify cell barcodes, ensuring that each cell’s data is correctly assigned. It then collapses Unique Molecular Identifiers (UMIs) to eliminate PCR duplicates and other biases, allowing for a more accurate representation of the original sample.
One key feature of NanoGen is its ability to call consensus at the base level, rather than at the sequence level, providing a more precise identification of mutations. NanoGen applies a negative binomial model to distinguish between true mutated reads and background noise, improving the reliability of mutation calls. To address the issue of mutated allele dropout, NanoGen imposes a strict threshold: it requires at least five distinct wild-type (WT) UMIs for a cell to be confidently classified as wild-type (WT).
First, we identify and retain only mutations that overlap with the WES data. For each candidate mutation, we calculate its cellular fraction. Next, we filter out any genes with fewer than 50 genotyped cells.
Information from the log files:
Below, we present the correlation between the cellular fraction, defined as VAF * 2, in WES and the cellular fraction in the target enrichment. As demonstrated, the target enrichment effectively recapitulates the cellular fraction observed in WES.
Next, we quantified the number of cells with multiple genotyped mutations, as shown in the tables below. We present the results for different mutation combinations. For this specific samples, we will look at these genes since they are the most genotyped: BAX ADCK1 DPF2 IPO9 RUNX1
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] stringi_1.5.3 ReactomePA_1.34.0 clusterProfiler_3.18.1
[4] org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0 IRanges_2.24.1
[7] S4Vectors_0.28.1 Biobase_2.50.0 BiocGenerics_0.36.1
[10] plotly_4.9.3 maftools_2.6.05 mclust_5.4.7
[13] SeuratObject_4.0.0 Seurat_4.0.0 forcats_0.5.1
[16] stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4
[19] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6
[22] ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] reticulate_1.18 tidyselect_1.1.0 RSQLite_2.2.3
[4] htmlwidgets_1.5.3 grid_4.0.3 BiocParallel_1.24.1
[7] Rtsne_0.15 scatterpie_0.1.5 munsell_0.5.0
[10] codetools_0.2-18 ica_1.0-2 DT_0.17
[13] future_1.21.0 miniUI_0.1.1.1 withr_2.4.1
[16] colorspace_2.0-0 GOSemSim_2.16.1 knitr_1.31
[19] rstudioapi_0.13 ROCR_1.0-11 tensor_1.5
[22] DOSE_3.16.0 listenv_0.8.0 labeling_0.4.2
[25] git2r_0.28.0 polyclip_1.10-0 bit64_4.0.5
[28] farver_2.0.3 rprojroot_2.0.2 downloader_0.4
[31] parallelly_1.23.0 vctrs_0.3.6 generics_0.1.0
[34] xfun_0.21 R6_2.5.0 graphlayouts_0.7.1
[37] spatstat.utils_2.0-0 cachem_1.0.3 fgsea_1.16.0
[40] assertthat_0.2.1 promises_1.1.1 scales_1.1.1
[43] ggraph_2.0.4 enrichplot_1.10.2 gtable_0.3.0
[46] globals_0.14.0 goftest_1.2-2 tidygraph_1.2.0
[49] workflowr_1.6.2 rlang_0.4.10 splines_4.0.3
[52] lazyeval_0.2.2 broom_0.7.4 checkmate_2.0.0
[55] BiocManager_1.30.10 yaml_2.2.1 reshape2_1.4.4
[58] abind_1.4-5 modelr_0.1.8 crosstalk_1.1.1
[61] backports_1.2.1 httpuv_1.5.5 qvalue_2.22.0
[64] tools_4.0.3 ellipsis_0.3.1 RColorBrewer_1.1-2
[67] ggridges_0.5.3 Rcpp_1.0.6 plyr_1.8.6
[70] rpart_4.1-15 deldir_0.2-9 pbapply_1.4-3
[73] viridis_0.5.1 cowplot_1.1.1 zoo_1.8-8
[76] haven_2.3.1 ggrepel_0.9.1 cluster_2.1.0
[79] fs_1.5.0 magrittr_2.0.1 data.table_1.13.6
[82] scattermore_0.7 DO.db_2.9 reactome.db_1.74.0
[85] lmtest_0.9-38 reprex_1.0.0 RANN_2.6.1
[88] fitdistrplus_1.1-3 matrixStats_0.58.0 hms_1.0.0
[91] patchwork_1.1.1 mime_0.9 evaluate_0.14
[94] xtable_1.8-4 readxl_1.3.1 gridExtra_2.3
[97] compiler_4.0.3 KernSmooth_2.23-18 crayon_1.4.1
[100] shadowtext_0.0.7 htmltools_0.5.1.1 mgcv_1.8-33
[103] later_1.1.0.1 lubridate_1.7.9.2 DBI_1.1.1
[106] tweenr_1.0.1 dbplyr_2.1.0 rappdirs_0.3.3
[109] MASS_7.3-53 Matrix_1.3-2 cli_2.3.0
[112] igraph_1.2.6 pkgconfig_2.0.3 rvcheck_0.1.8
[115] xml2_1.3.2 rvest_0.3.6 digest_0.6.27
[118] sctransform_0.3.2 RcppAnnoy_0.0.18 graph_1.68.0
[121] spatstat.data_2.0-0 rmarkdown_2.6 cellranger_1.1.0
[124] leiden_0.3.7 fastmatch_1.1-0 uwot_0.1.10
[127] shiny_1.6.0 graphite_1.36.0 lifecycle_0.2.0
[130] nlme_3.1-152 jsonlite_1.7.2 viridisLite_0.3.0
[133] pillar_1.4.7 lattice_0.20-41 fastmap_1.1.0
[136] httr_1.4.2 survival_3.2-7 GO.db_3.12.1
[139] glue_1.4.2 spatstat_1.64-1 png_0.1-7
[142] bit_4.0.4 ggforce_0.3.2 blob_1.2.1
[145] memoise_2.0.0 irlba_2.3.3 future.apply_1.7.0